When was the last time you grew? No, not your height! Your skills, experience, knowledge, and qualifications. Those should be growing and evolving regularly. We all have a toolbox and we should all be giving it a regular boost and update.
If you are looking into how to become a data analyst, and want to add incredible new skills to your box of tools, you will find this article useful.
What is a data analytics tool?
Whether you're a writer, builder, programmer, artist - whichever industry you currently work in - we all have tools that help us with our work. These might be physical or digital tools. They might be hardware or software.
Data Analytics Tools are no different. Data analysts are always on the lookout for tools that will make their work easier and faster.
The best data analytics tools:
- Are intuitive and easy to use
- Automate the repetitive tasks
- Have a range of capabilities like cleaning and predictive modelling
- Have great support and a collaborative community
In this article, we discuss some data analytics tools that tick all or some of those features and will give you a starting point in what to learn. You'll also find resources on how to use the tools and start your own projects. If you are currently working as a data analyst you might find some useful information that you can implement in your current role and company.
Google Analytics
History: Did you know that Google Analytics was initially called Urchin Analytics, after the company that created it? Google bought Urchin Analytics in 2005, and it eventually became known as Google Analytics. You can read more about its history here.
Google Analytics helps website owners track the number of visitors their website gets. It also includes data on how long each page is viewed, which pages were looked at, and some insight into the demographics of each visitor.
Google Analytics is incredibly useful for businesses to see what's working on their website and what isn't.It helps to identify which content is being read and which pages are converting, it also shows which pages are causing visitors to leave and thus which areas to improve.
Here's a list of 10 ways google analytics helps businesses perform better.
Google Analytics offers a lot of insights, which means a lot of data, graphs, stats, and actionable items. And of course, nearly every business has a website.
However, not all business owners and marketing teams have the time or expertise to make full use of this invaluable resource and so having an expert on board would be extremely valuable for them. ( need a source with metrics)
If you decide to become a Google Analytics Expert, there's a good chance of having lots of regular work.
Google Analytics has its own free training available, which you can find here and is worth a look at.
Of course, as with most skills, the best way to learn this is by doing. Why not start a website yourself, or find a friend or family member who has one, and offer to help with data analytics tools and see if you can help grow their business.
Cost: It's free!
https://www.j2store.org/blog/digital-marketing/10-reasons-why-you-need-google-analytics-for-business-growth.html
Data Analytics in Excel
OpenRefine for data cleaning
History: In 2010, Google bought a project called freebase gridworks and renamed it GoogleRefine, however they stopped supporting it in 2012 and so it became an open-source project and is now known as OpenRefine.
Source: https://libguides.uidaho.edu/openrefine
What is it: OpenRefine is a powerful piece of software that helps you work with messy data. You can use it for data cleaning, data transformation, and also to extend your data. Read more about its uses here.
What it's for: Sometimes you have a spreadsheet filled with information about a project, but the data in the sheet is a mess. For example, let's say you are looking at sales data for a sunglasses company.
The 'brand' column should state the name of the designer of each pair of sunglasses sold. Instead, the users have entered a mix of things, sometimes the full name, sometimes a shortened version, and sometimes an acronym.
E.g Throughout the spreadsheet Ray-Ban sunglasses have been entered as Rayban, Ray-ban, Rb, R-b, and so on.
Open refine has a feature that lets you select which names all mean the same thing and then updates the data accordingly.
This makes data cleaning much easier and faster.
Why it's worth learning: Data is messy.
Trying to tidy up and use data like that in a spreadsheet takes hours and is frustrating.
OpenRefine saves a lot of time.
If you can master a tool that helps your company work more efficiently you are more likely to stand out as a candidate during the interview process.
It's easy to learn, and you can find open-source data to play around with and add to your portfolio.
Here's a great article on the benefits of using OpenRefine for real problems with data cleaning.
How you can master it:
Start at the main website: openrefine.org, watch the videos, and see what kind of projects it's being used for.
It's free! So download it and start creating your projects.
Sources: http://www.davidhuynh.net/spaces/nicar2011/tutorial.pdf
Tableau Public for getting started with visualizations (to the public)
History: Innovation and data are linked. As data increases and gets richer, businesses need to innovate and continually improve how they make use of this data.
Tableau was founded in 2004 to help businesses stay innovative and meet the ever-growing need of combining data and innovation.
In 2010 they launched Tableau Public to help make data analysis accessible to more people and to make data itself interactive - goodbye static pdfs! You can read more about their aims here.
They've recently reached their 2 millionth user, so definitely worth checking out.
What is it: Tableau Public is a free platform and online community where you can create stunning data visuals and share them with members.
What it's for: You can use Tableau public to learn how to make different types of visualizations, improve your data analytics skills and build up your portfolio.
Tableau Public helps you to:
- Explore possibilities with data and make it interactive
- Stay motivated as you become part of a community of data lovers
- Create custom maps and charts without coding
- Look at existing visualizations to see how they were made
- Practice your skills with fun challenges
- Advance your career and stand out from other applicants
You can learn more about Tableau Public on their main site.
Why it's worth learning:
Tableau Public is a popular tool and looks like it's here to stay.
It currently has a 6% share of the data visualization market and is used by a range of small to large companies.
Here are some more market stats about Tableau Public, so you can see which companies use it and in which industries.
How you can master it:
Head over to Tableau Public's website and sign up for free. You will have everything you need including projects, challenges, and community support.
Dataiku Community for data preparation and machine learning
History: Dataiku was founded in 2013 with the mission of helping businesses use data to stay innovative. They have been very successful and launched Dataiku Community 2 years ago in 2020.
What it's for: Dataiku community is a place for data scientists to learn, share, and collaborate: "the Dataiku Community has been the one-stop destination for peer-to-peer support and learning about Dataiku and data science".
It's the place to go to learn about data analytics and data science. You can read more about Dataiku Community on their blog.
Why it's worth using:
- Dataiku community has local user groups where you can network, share your expertise and expand your knowledge.
- You'll get to meet Datiaku Neurons - the experts in the Community who contribute a lot of useful information and experiences.
- You'll have a lot of support. If you are changing careers or just starting, you don't need to go it alone. Being part of the Dataiku community will help you build your portfolio and meet lots of like-minded data analysts.
How you can get involved: Go sign up!
Start contributing to the community. It doesn't matter if you are a newbie, share what you are learning and what you hope to achieve.
There are bound to be other data analysts in a similar position to you who will find your experiences useful.
Make connections and ask questions.
Helping others is super rewarding and in turn, helps you to become successful too.
Orange Data Mining
History: Orange was started back in 1997 by Donald Michie. He wanted to create an open toolbox for machine learning. Michie put forward the idea that that toolbox should be a web application where people can submit data mining code, scripts, and data and share everything in an online workspace.
You can read the full history of orange here and see some of their first, very 90s, screenshots!
What it's for: Orange Data Mining is a platform for machine learning and of course data mining. It is open source and consists of lots of C++ scripts that are used to run common algorithms used to manipulate data.
Along with its vast library of scripts it also provides a graphical user interface and the ability to create a range of visualizations.
Orange is commonly used in genomic research, biomedicine, and teaching.
Why it's worth using:
- Orange provides interactive data visualizations helping you deep dive into results and data sets
- You can get a lot done without coding
- Lots of add-ons are available so you can expand its functionality
How you can master it:
As with any tool, the best way to learn is to start using it and you can download Orange here.
However Orange does have a useful set of tutorials on youtube that you might find helpful: https://www.youtube.com/watch?v=HXjnDIgGDuI
They also run workshops and have a resources section on their website.
KNIME
History: The Knime Analytics platform was first released in July 2006. It is an open-source tool and quickly became wildly popular with the open-source community as well as pharmaceutical companies.
Today there are over 1700 companies that use Knime and most of them make over $1000 million in revenue. So if you've got aspirations to work for large companies, it's worth getting to know Knime.
You can see more stats and industry usage data here.
What it's for: Knime claims to be the open-source tool for creating data science.
That's a massive vision for a data analytics tool.
These are the things Knime provides to give its users a complete data science workflow:
- You can combine data from any source
- Manipulate your data in every way possible, from cleaning to deriving stats
- Machine learning models with optimization and validation
- A full range of data visualizations
Why it's worth learning:
Knime is popular with large companies in a range of different industries, so adding this data analytics tool to your skill set and project portfolio would be beneficial.
One of its main benefits is that it offers visual workflows, numerous add-ons, and machine learning capabilities. It ticks a lot of boxes for data analysts and scientists and seems like this tool is going to keep growing and increase in popularity.
How to master Knime:
Although Knime is open source and free, it might not be the most intuitive and easy to learn of the data analytics tools discussed.
But don't let that put you off, there are several free resources and courses that will help you:
Youtube: https://www.youtube.com/watch?v=Pom9AuI9yg4
Linkedin: https://www.linkedin.com/learning/introduction-to-machine-learning-with-knime/open-source-machine-learning-with-knime?autoplay=true
Nov 10, 2021
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